Coregistration of Prechemotherapy PET-CT for Planning Pediatric Hodgkin's Disease Radiotherapy Significantly Diminishes Interobserver Variability of Clinical Target Volume Definition

Metwally, Hussein; Courbon, F.; David, Isabelle; Filleron, Thomas; Blouet, A.; Rives, M.; Izar, F.; Zerdoud, Slimane; Plat, Geneviève; Vial, J.; Robert, Alain; Laprie, Anne

doi:10.1016/j.ijrobp.2010.02.024

Cited by 29 publications

(15 citation statements)

References 46 publications

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“…The magnitude of this uncertainty is determined by several factors such as images used for delineation (image modality, resolution, contrast, and windowing), human factors (experience of observers, their professional background), and the presence of the guidelines or atlases for contouring [3,4,11,32,35,43,50,52]. The introduction of multimodality imaging in planning and delivery process (CT, MR, PET-CT) gives further promise of better target definition [16,19,26,30,34,38,44,45,47].…”

Section: Critical Discussion Of Evaluation Parameters For Inter-obsermentioning

confidence: 99%

“…The first group is based on descriptive statistics and contains parameters describing the distribution of volumes, such as mean, standard deviation, standard error, range, ratio of the largest drawn volume to the smallest one, and dispersion of the distribution, i.e., coefficient of variation (COV). COV is a popular parameter [2,13,19,25,26,43,51] as it provides the measure of dispersion of the distribution in a single number and can be easily correlated with other parameters of interest.…”

Section: Critical Discussion Of Evaluation Parameters For Inter-obsermentioning

confidence: 99%

“…Due to the variety of used metrics (common to encompassing volume ratio, Jaccard index, or Dice similarity coefficient, etc. ), it represents the domain where harmonization in reporting is lacking [2,5,6,8,10,19,22,24,25,26,29,32,34,37,38,41,42,44,45,46,48,51,52,53]. When moving to 3D representation, metrics such as variation of the center of mass (COM) allow the evaluation of the volume displacement in space [3,10,25,37,46,47,52,53].…”

Section: Critical Discussion Of Evaluation Parameters For Inter-obsermentioning

confidence: 99%

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Critical discussion of evaluation parameters for inter-observer variability in target definition for radiation therapy

et al. 2012

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Section: Critical Discussion Of Evaluation Parameters For Inter-obsermentioning

confidence: 99%

Section: Critical Discussion Of Evaluation Parameters For Inter-obsermentioning

confidence: 99%

Section: Critical Discussion Of Evaluation Parameters For Inter-obsermentioning

confidence: 99%

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Critical discussion of evaluation parameters for inter-observer variability in target definition for radiation therapy

et al. 2012

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“…In radiotherapy treatment planning, significant variability can occur when multiple observers contour the target volume . This inter‐observer variability has been shown to be reduced by combining information from multimodality imaging and performing single delineations on fused images, such as CT and PET, or MRI and PET . However, traditional visual assessment of multimodality images is subjective and prone to variation.…”

Section: Description and Classification Of The Algorithmsmentioning

confidence: 99%

Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

et al. 2017

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Purpose The purpose of this educational report is to provide an overview of the present state-of-the-art PET auto-segmentation (PET-AS) algorithms and their respective validation, with an emphasis on providing the user with help in understanding the challenges and pitfalls associated with selecting and implementing a PET-AS algorithm for a particular application. Approach A brief description of the different types of PET-AS algorithms is provided using a classification based on method complexity and type. The advantages and the limitations of the current PET-AS algorithms are highlighted based on current publications and existing comparison studies. A review of the available image datasets and contour evaluation metrics in terms of their applicability for establishing a standardized evaluation of PET-AS algorithms is provided. The performance requirements for the algorithms and their dependence on the application, the radiotracer used and the evaluation criteria are described and discussed. Finally, a procedure for algorithm acceptance and implementation, as well as the complementary role of manual and auto-segmentation are addressed. Findings A large number of PET-AS algorithms have been developed within the last 20 years. Many of the proposed algorithms are based on either fixed or adaptively selected thresholds. More recently, numerous papers have proposed the use of more advanced image analysis paradigms to perform semi-automated delineation of the PET images. However, the level of algorithm validation is variable and for most published algorithms is either insufficient or inconsistent which prevents recommending a single algorithm. This is compounded by the fact that realistic image configurations with low signal-to-noise ratios (SNR) and heterogeneous tracer distributions have rarely been used. Large variations in the evaluation methods used in the literature point to the need for a standardized evaluation protocol. Conclusions Available comparison studies suggest that PET-AS algorithms relying on advanced image analysis paradigms provide generally more accurate segmentation than approaches based on PET activity thresholds, particularly for realistic configurations. However, this may not be the case for simple shape lesions in situations with a narrower range of parameters, where simpler methods may also perform well. Recent algorithms which employ some type of consensus or automatic selection between several PET-AS methods have potential to overcome the limitations of the individual methods when appropriately trained. In either case, accuracy evaluation is required for each different PET scanner and scanning and image reconstruction protocol. For the simpler, less robust approaches, adaptation to scanning conditions, tumor type, and tumor location by optimization of parameters is necessary. The results from the method evaluation stage can be used to estimate the contouring uncertainty. All PET-AS contours should be critically verified by a physician. A standard test, i.e., a benchmark dedicated to ...

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“…Some studies evaluated the variability in target volume delineation using descriptive statistics and parameters describing the distribution of volumes and diameters, such as mean, standard deviation, range, and dispersion of the distribution, such as COV [21][22][23][24][25][26][27][28]. The majority of studies deal with approaches that describe the overlapping area between contoured volumes, using metrics, such as CI and DC [22][23][24][26][27][28][29][30][31]. Moreover, when moving to 3D representation, metrics, such as variation of the COM, allow to evaluate the volume displacements in space [27,32,33].…”

Section: Discussionmentioning

confidence: 99%

Delineation of the larynx as organ at risk in radiotherapy: a contouring course within “Rete Oncologica Piemonte-Valle d’Aosta” network to reduce inter- and intraobserver variability

et al. 2016

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Mean percentage of intersection and DC strongly increased after the course, rising from 57.76 to 93.83 % and from 0.68 to 0.89, respectively. CI enhanced from 0.06 to 0.31. Conclusions This study shows an improvement in larynx definition after the contouring course with lower interobserver variability and major consistency compared to the reference volume. Other specific educational activities may further increase the quality of radiation therapy contouring in this setting.

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Coregistration of Prechemotherapy PET-CT for Planning Pediatric Hodgkin's Disease Radiotherapy Significantly Diminishes Interobserver Variability of Clinical Target Volume Definition

Cited by 29 publications

References 46 publications

Critical discussion of evaluation parameters for inter-observer variability in target definition for radiation therapy

Critical discussion of evaluation parameters for inter-observer variability in target definition for radiation therapy

Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

Delineation of the larynx as organ at risk in radiotherapy: a contouring course within “Rete Oncologica Piemonte-Valle d’Aosta” network to reduce inter- and intraobserver variability

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